How we used AI to identify paid posts on Facebook

Social media platforms – especially Facebook – have been the big growth story in advertising in recent years. Social media offers high reach, fine targeting, and strong engagement and relevance. It has generated a huge amount of data on consumer behaviour, but much of this data remains within the confines of the platforms, which are keen to protect users’ privacy and their own intellectual property and competitive advantage. We can use AI techniques to unlock some of this data and make use of it for our clients.

Using the proprietary Publicis Media application Socialtools, we can track and analyse the performance of our own clients’ posts on Facebook, distinguishing between those we have paid to promote, and those that have achieved their reach organically. What has not been possible without the application of machine learning is the ability to distinguish between paid and unpaid posts for competing brands. Using data from more than 300,000 posts collected via Socialtools – including likes, comments, shares, time delay between post and comment, day of week, and impressions/paid impressions – our data team have been able to answer this question.

We trained the model using data from November 2015 to November 2016, then tested it on data from December 2016 to early January 2017, which included 37,000 posts. Using Random Forest classifiers, which test the different scenarios for the highest accuracy, our model correctly predicted that 29,395 were unpaid and that 4,390 were paid. This represents an accuracy level of 92%, and in one simple exercise clearly illustrates that AI techniques can offer genuine opportunities to derive insight from data that would otherwise be too complex or inaccessible.